The invention relates generally to modeling customer behavior. In particular, the invention relates to developing a propensity model for a customer's behavior based on past interactions with a plurality of customers and having a low event rate.
Good customer relationship management (CRM) has become a valuable tool in today's highly competitive markets. CRM enables a business to know who its customers are, what its customers buy, how much its customers earn, how much its customers spend and other similar types of information that allow a company to understand the market for its goods and/or services. This information also helps a company to find out the propensity of its customer for a particular behavior, such as how likely the customer is to respond to an offer, how likely the customer is to default on a loan, or pay off a loan early, etc. One method of predicting customer behavior that businesses have used is to build a propensity model from data obtained from experience with existing customers. For this purpose, companies maintain databases of their customers replete with data from previous transactions, conduct surveys, or customer response sheets.
One problem associated with propensity modeling in consumer finance business is that the rate that an event of interest has occurred, the event rate, in some cases may turn out to be very low. However, the economic benefits even with a low success rate may justify pursuing the opportunity in the future. For example, an organization may send out thousands, perhaps millions, of offers in the mail, but only a small percentage of customers may respond. Indeed, some of the data used in propensity modeling may have an event rate of less than one percent. This means that for an offer made to one thousand customers less than ten customers actually responded to the offer. This makes the development of an accurate propensity model for this type of event very difficult. Existing propensity modeling techniques that attempt to overcome the problems caused by a low event rate have resulted in models that memorize specific characteristics of the training sample. These models do not have good generalization abilities for the population of data as a whole.
Therefore, there is a need for a technique that improves an organization's ability to target specific customers most likely to engage in an event when the rate of the event for all customers is low based on past performance. In particular, a technique is desired that would enable an accurate propensity model of customer behavior to be developed for activities having a low event rate.